As far as we know, no work has leveraged the conversational structure of Twitter postings for stance classification, and hence its utility remains unexplored. A work that is related is that of Lukasik et al. lukasik2016hawkes, who exploited the temporal sequence of tweets, although the conversational structure was ignored and each tweet was treated as a separate unit. In other domains where debates or conversations are involved, the sequence of responses has been exploited to make the most of the evolving discourse and perform an improved classification of each individual post after learning the structure and dynamics of the conversation as a whole. For instance, Qu and Liu qu2011finding found Hidden Markov Models to be an effective approach to classify threads in on-line fora as successfully solving or not the question raised in the initial post. This was later further studied in a SemEval shared task, where each post in a forum thread had to also be classified as good, potential or bad [1]}. FitzGerald et al. fitzgerald2011exploiting used a linear-chain CRF to identify high-quality comments in threads responding to blog posts.
